edeno / jadhav-2016-data-analysis Goto Github PK

Keeps a spike from being so informative that it forces the decoding filter to immediately choose a state and stay there

There are tetrodes labeled CA1Ref, PFCRef, but it's not clear if they are using both references, one reference, or if this is just a potential marker for reference electrode.

So we can compare ripples at the beginning of the session to ripples at the end of the session. Related to issue #47.

More informative name

Check if there's any evoked potential from the ripple event inducing coherence in PFC

See if that affects the ripple triggered average LFP

Rhythms of the ripple start times
Are there enough ripples?

Currently, I am excluding all CA1 neurons with zero spikes. The problem is that for low spiking neurons, the training data (times when the rat is running > 4 cm/s) itself might not have spikes.

Gives us a better idea of the information content of each ripple and our ability to decode

• spiking vs no spiking replay events coherence
• first half of the experiment ripples vs second half ripples
• forward vs. reverse
• inbound vs. outbound

Such as whether it was rewarded or not rewarded, inbound or outbound

Sanity check on detection method

Compute coherence phase vs frequency and look a linear component.

I think data_processing is probably a better name for a module that involves extracting data from their data structures and reshaping it as necessary

There are a number of frequencies that we could target with our spectral analysis:

• Ripple frequency (150-250 Hz)
• High Gamma (50-100 Hz)
• Low Gamma (30-50 Hz)
• Beta (12-30 Hz)
• Theta/Alpha (4-12 Hz)

Loren thinks that Low Gamma and Theta are of more interest in a coherence analysis. He's worried that increased ripple frequency power simply reflects an increase in spiking from increased input to both areas.

He also suggested that we look at spectrograms in PFC and maybe use that to determine what frequencies to look at.

In order to make sure I understand the data formatting and analysis in the original paper, need to reproduce at least one analysis from the Jadhav paper. Two candidate analyses are:

• Figure 3 - show PFC activity locked to the time of the short wave ripple
• Figure 6 - the GLM decoding of PFC spiking using the CA1 ensemble

I would like to make the switch to Python, but the filter framework is written in Matlab. The framework might be hard to parallelize over in the future and it has a bunch of 'eval' statements that make it hard for Matlab to optimize the code. On the other hand, it's already written and I know it'll give me data in the same format as the original paper.

Have not seen a data structure that allows you to get the start and stop times for each trial

1. High frequency difference in coherence
2. Low frequency difference in coherence
3. Ripple triggered averages
4. Group delay?

The animals start with a novel track and then learn over days. Need to characterize:

• time between reward wells for each session
• amount rewarded for each session
• how these change relative to inbound / outbound trials and over sessions/days

Should expect time between reward wells to get faster over sessions/days and amount rewarded to increase over sessions/days. Inbound trials should be faster than outbound trials.

See:

Geweke, J. 1982 Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304-313.

Xinyi's model assumes that outbound reverse movements are the same as inbound forward movements in the state transition matrix. We could improve this by computing the reverse time trajectory.

• data_processing.py
• ripple_detection.py
• spectral.py
• ripple_decoding.py
• analysis.py
• test_data_processing.py
• test_ripple_detection.py
• test_spectral.py
• test_ripple_decoding.py
• test_analysis.py

The DIO file has six channels corresponding to motion detection (the animal is near the reward well) or reward delivery for each of the three reward wells located at the end of each arm. So the animals' position (as marked by infrared diodes on top of the microdrive preamp) when these channels are active should correspond to the end of the arms where the reward well is located. However, this is not always the case:

Blue lines indicate the rat's position in the W-track. Red dots correspond to the rat's position when either motion detection or reward delivery is triggered. Data is from rat 'HPa', day 8, epoch 2. See python notebook UnderstandingDIO for more plots and code.

I emailed Demetris and he confirmed there might be an issue:

Unfortunately you will have to ask shantanu about the other issues as I seem to remember him saying something about his dio pules being corrupted for a couple of the animals leading to the displaced position/time issue that you're seeing (so don't worry you're not crazy :)). By now, he may have manually corrected these pulses or reconstructed the dio-like info from the position or something.

I will email Shantanu and see if he has any solutions

• Inbound trials vs. Outbound trials
• Prospective vs. Retrospective Replay
• Right vs Left turn
• Local vs. Remote environment (Remote = some other environment the animal has previously been in)

Conjugate of complex spectrum in one frequency times the complex spectrum of another . Identifies the ability of phase / magnitude in one frequency to predict the other.

Also look at residuals

Eventually we will have to scale up to process all the day. One possibility is using dask. Examples:

• Example1
• Example2

Could be useful for checking the quality of decodes for a large dataset and for demonstration of the algorithm itself.

Look for spectral features of PFC LFPs

Our version of their code used their description of ripple detection in
Karlsson, M.P., Frank, L.M., 2009. Awake replay of remote experiences in the hippocampus. Nature Neuroscience 12, 913–918. doi:10.1038/nn.2344:

SWRs were identified on the basis of peaks in the LFP recorded from one channel from each tetrode in the CA3 and CA1 cell layers. The raw LFP data were bandpass-filtered between 150–250 Hz, and the SWR envelope was determined using a Hilbert transform. The envelope was smoothed with a Gaussian (4-ms s.d.). We initially identified SWR events as sets of times when the smoothed envelope stayed above 3 s.d. of the mean for at least 15 ms on at least one tetrode. We defined the entire SWR as including times immediately before and after that threshold crossing event during which the envelope exceeded the mean. Overlapping SWRs were combined across tetrodes, so many events extended beyond the SWR seen on a single tetrode.

Which is what Jadhav 2016 uses as well. But a different detection method is presented in Kay, K., Sosa, M., Chung, J.E., Karlsson, M.P., Larkin, M.C., Frank, L.M., 2016. A hippocampal network for spatial coding during immobility and sleep. Nature 531, 185–190. doi:10.1038/nature17144:

Detection of SWRs was prerequisite for all data analysed in this study, and was performed only when at least three CA1 cell layer recordings were available. Offline, a multisite average approach was used to detect SWRs58. Specifically, LFPs from all available CA1 cell layer tetrodes were filtered between 150–250 Hz, then squared and summed across tetrodes. This sum was smoothed with a Gaussian kernel (σ = 4 ms) and the square root of the smoothed sum was analysed. SWRs were detected when the signal exceeded 2 s.d. of the recording epoch mean for at least 15 ms. SWR periods were then defined as the periods, containing the times of threshold crossing, in which the power trace exceeded the mean. SWR onset was defined as the start of a SWR period. Detection of SWRs was performed only when subjects’ head speed was <4 cm s−1. For SWR-triggered spike raster plots and PSTH plots, a 0.5 s exclusion period was imposed to isolate SWRs occurring only after non-SWR periods; otherwise, analyses of SWRs included all detected SWRs.

In the EEG folder, there are several types of files:

• files that correspond to the raw LFP recordings ({animal}eeg{day}-{epoch}-{tetrode}).mat
• files that correspond to the ground LFP recordings ({animal}eeggrnd{day}-{epoch}-{tetrode}.mat
• files that correspond to the processed LFP in different frequency bands (e.g. {animal}delta{day}-{epoch}-{tetrode}

But there doesn't seem to be a file for each tetrode. I'm not sure why.

To further complicate things, there is an OriEEG folder, which I assumed meant "Original EEG" files, but seems to only include tetrodes 17 and above. These seem to be only PFC electrodes (at least for the first animal).

I'm going to try to:

1. See if the Filter framework code gives any clues
2. Maybe email Demetris from Loren's lab

Use occupancy normalized histograms overlayed on model fit
Maybe simulate data from the model and compare to the real data
Maybe compare to Xinyi’s data

Clusterless decoding might give us more spikes per ripple to work with (because it includes spikes that couldn't be assigned to a particular neuron)